This book supplies a unifying framework for the derivation of
probabilistic membership values in any classification task. While
statistical classifiers usually generate such probabilities which
reflect the assessment uncertainty, regularization methods supply
membership values which do not induce any probabilistic confidence.
It is desirable, to transform or re-scale membership values to
membership probabilities, since they are comparable and easier
applicable for post-processing. In this book several univariate
calibration methods are presented. The methods are compared by
their performance in experiments measured in terms of correctness
and well-calibration. Multivariate extensions for regularization
methods usually use a reduction to binary tasks, followed by
univariate calibration and further application of the pairwise
coupling algorithm. This book introduces a well-performing
alternative to coupling that generates Dirichlet distributed
membership probabilities. This flexible one-step algorithm bases on
probability theory and is applicable to all classification
problems. Dirichlet calibration method and pairwise coupling are
compared in further experiments.
General
Imprint: |
Sudwestdeutscher Verlag Fur Hochschulschriften AG
|
Country of origin: |
United States |
Release date: |
June 2010 |
First published: |
June 2010 |
Authors: |
Martin Gebel
|
Dimensions: |
229 x 152 x 8mm (L x W x T) |
Format: |
Paperback - Trade
|
Pages: |
140 |
ISBN-13: |
978-3-8381-1224-4 |
Categories: |
Books >
Science & Mathematics >
Mathematics >
Probability & statistics
|
LSN: |
3-8381-1224-5 |
Barcode: |
9783838112244 |
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